Insertional Mutagenesis as a Tool for Identifying and Characterizing Novel Candidate Cancer Genes
Cancer results from dynamic changes in a set of cellular genes. Mutations in oncogenes and tumor suppressor genes are responsible for converting a normal cell into a malignant one. Much research has been done to identify a large number of oncogenes and tumor suppressor genes that are frequently mutated in human cancers. These efforts suggest that the cancer genome is composed of a few commonly mutated genes along with hundreds of infrequently mutated genes. Deciphering which of the mutations are important in tumorigenesis will aid in understanding the biology of cancer as well as provide potential new therapeutic targets.
Mouse models of human cancer have been used to determine the significance genetic alterations identified in human cancers. One tool that has been used for this purpose in the mouse is insertional mutagenesis. In our study, we designed a Sleeping Beauty transposon-based forward genetic screen in mice to identify candidate genes for breast cancer in the presence and absence of Cav1 mutations. Analysis of transposon integrations in all mammary tumors identified 210 common insertion sites (CISs) and candidate (CAN) genes were assigned to many of the CISs. Multiple CAN genes have been previously implicated in human breast cancers. Identifying the roles of these 210 CAN genes in tumorigenesis will determine their significance in human breast cancer.
In this study, we also investigated the role of a transcription factor, Meis1, which was originally identified in murine leukemias caused by retroviral-induced insertional mutagenesis. Overexpression of Meis1 in conjunction with HoxA9 is detected in a variety of myeloid leukemia cell lines and primary human samples of acute myeloid leukemia (AML). In an effort to identify additional downstream targets of Meis1, we generated FDC-P1 cells overexpressing Meis1 in the presence or absence of HoxA9. Microarray analysis was performed on RNA isolated from these cells. A combination of bioinformatics and statistical analyses were used to look for genes with differential regulation in the presence or absence of Meis1 and HoxA9. These data were used to generate a genetic signature characteristic of Meis1 expression and shed light onto other pathways that may involve Meis1 and HoxA9.